Automatic Prediction of Surface Phase Diagrams Using Ab Initio Grand Canonical Monte Carlo

The properties of a material are often strongly influenced by its surfaces. Depending on the nature of the chemical bonding in a material, its surface can undergo a variety of stabilizing reconstructions that dramatically alter the chemical reactivity, light absorption, and electronic band offsets. For decades, ab initio thermodynamics has been the method of choice for computationally determining the surface phase diagram of a material under different conditions. The surfaces considered for these studies, however, are often human-selected and too few in number, leading both to insufficient exploration of all possible surfaces and to biases toward portions of the composition–structure phase space that often do not encompass the most stable surfaces. To overcome these limitations and automate the discovery of realistic surfaces, we combine density functional theory and grand canonical Monte Carlo (GCMC) into “ab initio GCMC.” This paper presents the successful application of ab initio GCMC to the study of o...